RT Journal Article
T1 Large-scale system identification using self-adaptive penguin search algorithm
A1 Udaichi, Karthikeyan
A1 Chinaveer Nagappan, Ravi
A1 García Torres, Miguel
A1 Bidare Divakarachari, Parameshchari
A1 Nayak Bhukya, Shankar
K1 Penguin search algorithm
K1 System identification
AB From an engineering point of view, non-linear systems are essential to the operation ofcontrol systems, because all systems actually have a non-linear state in nature. In reality,there are many different kinds of non-linear systems hidden by this negative definition.For successful analysis and control, the identification of non-linear systems using unknownmodels is typically necessary. Till now, numerous approaches are developed for identifyingnon-linear systems, but it cannot be employed with a large number of components. More-over, system identification is typically restricted to output and input signals alone, alsosuch systems are rarely used in reality. This is the primary justification for using non-linearsystems in this research. So, this research proposed a non-linear model of system iden-tification for large-scale systems under the consideration of two systems: bilinear systemand Volterra system. Therefore, a novel algorithm named Self Adaptive Penguin SearchOptimization (SAPeSO) is introduced to attain the system characteristics properly andminimize the output variation. Finally, the effectiveness of the proposed work is com-pared with existing works in terms of various error measures. This research mainly focuseson the application-oriented engineering problems. In particular, the Mean Absolute Error(MAE) of the proposed work for the Volterra system at 4000 samples is 18.83%, 14.05%,8.88%, 29.72%, 19.91%, and 6.70% which is better than the existing bald eagle search(BES), arithmetic optimization algorithm (AOA), whale optimization algorithm (WOA),nonlinear autoregressive moving average with exogenous inputs- frequency response func-tion+principal component analysis (NARMAX-FRF+PCA), Global Gravitational SearchAlgorithm-Assisted Kalman Filter (CGS-KF), and sparse regression and separable leastsquares method (SR-SLSM) methods, respectively. Finally, the error is minimum for theproposed model when compared with the other traditional approaches.
PB Wiley
YR 2023
FD 2023
LK https://hdl.handle.net/10433/19663
UL https://hdl.handle.net/10433/19663
LA en
NO IET Control Theory & Applications, vol. 17, nº 17, p. 2292-2303
NO Proyectos de investigaciónFECYT -- APRENDIZAJE PROFUNDO Y APRENDIZAJE ONLINE EXPLICABLES PARA SOST...PY20-00870UPO-138516
NO Deporte e Informática
DS RIO
RD May 9, 2026